TL;DR
RegionMed-CLIP is a novel region-aware multimodal contrastive learning model that improves medical image understanding by integrating localized pathological regions with global features, supported by a large annotated corpus.
Contribution
It introduces a region-aware contrastive framework with an adaptive ROI processor and a large-scale annotated dataset for enhanced medical image-text understanding.
Findings
Outperforms state-of-the-art models in retrieval and classification tasks.
Demonstrates the effectiveness of region-aware pre-training in medical imaging.
Enables robust zero-shot and VQA performance on medical datasets.
Abstract
Medical image understanding plays a crucial role in enabling automated diagnosis and data-driven clinical decision support. However, its progress is impeded by two primary challenges: the limited availability of high-quality annotated medical data and an overreliance on global image features, which often miss subtle but clinically significant pathological regions. To address these issues, we introduce RegionMed-CLIP, a region-aware multimodal contrastive learning framework that explicitly incorporates localized pathological signals along with holistic semantic representations. The core of our method is an innovative region-of-interest (ROI) processor that adaptively integrates fine-grained regional features with the global context, supported by a progressive training strategy that enhances hierarchical multimodal alignment. To enable large-scale region-level representation learning, we…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
1. Breadth of the dataset, with coverage of various medical imaging modalities and anatomical regions. 2. The model seems to outperform all methods it was compared to.
1. There is no evidence for the quality and reliability of the resulting annotations provided. 2. The baseline models are fairly old (the latest model is from 2023). It would be advisable to compare the model's effectiveness in comparison to more recent studies both within this domain as well as for generalist VLM models. 3. There is a significant number of inconsistencies in the references (a significant portion of references in the paper refers to non-existing works, including references to t
1. The architectural design builds upon ideas from Alpha-CLIP and UMG-CLIP in natural image domains—specifically, augmenting the standard CLIP framework with the ability to attend to local (region-level) information. To the best of my knowledge, this is the first such attempt in the medical imaging context. 2. The authors curate a multimodal dataset encompassing various levels of textual annotations and lesion mask annotations. They also commit to open-sourcing a subset of this dataset in the fu
### **Major Weaknesses** 1. While the introduction briefly mentions how the dataset was annotated, neither the main experiments nor the appendix fully disclose which public datasets were integrated to construct *MedRegion-500k*. This is a serious oversight. For a dataset assembled from multiple existing sources, it is essential to list all constituent datasets. If the list is lengthy, it should at least appear in the appendix; otherwise, readers cannot properly assess the validity or reproducib
- The paper introduces MedRegion-500k, a region-aware dataset (image-text pairs) with automatically extracted ROIs and multi-level captions (summary, detailed report, region caption, and negatives). This dataset is what enables fine-grained learning and it’s positioned as the key reason RegionMed-CLIP outperforms other models, even though it’s smaller in scale than datasets like PMC-15M or BiomedCLIP. - The paper extends the CLIP framework in a clear and logical way by combining whole-image and
- One noticeable weakness is that the paper doesn’t clearly explain how much human expertise actually went into validating the data. They mention that a “small subset” of the ROI crops and Qwen-generated captions were reviewed by medical experts, but they never say how many experts were involved, how many samples they checked, or what proportion of the 500k dataset was manually verified. As a result, it’s hard to judge the true reliability of the region annotations or captions; most of the datas
1) The idea of constructing a large scale medical image pre-training dataset with fine-grained annotation on ROI is reasonable. 2) The design of the proposed method that fuses local ROI features into global image feature in the pre-training is reasonable, because it provide some global context for understanding a local region.
1. It seems to me that a subset of references in the paper appear fabricated, misattributed, or improperly formatted. Specifically, I can not trace some of these references back to their origin or they seem to not exist in the stated vanue. Some of the arXiv IDs in the citation link to unrelated papers. 2. Important technical details are missing on the pre-trained framework of this paper. For example, this paper does not clearly explain how is the cross-attention in ROI processer is designed.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
